Breast cancer remains the second most common cancer in women, and is one of the few cancer sites for which incidence rates continue to rise. In addition, despite improvements in breast cancer survival, racial disparities in mortality remain pervasive. These mortality disparities have been widening over the last two decades, with the odds of dying from breast cancer for Black vs. White women increasing from 1.27 in 1984 to 1.85 in 1999. This racial inequality in breast cancer outcomes is cast on the backdrop of an obesity epidemic. We have assembled an experienced multidisciplinary team from Georgetown University, Einstein College of Medicine, Erasmus University, and RAND to use two established models (SPECTRUM and MISCAN) to develop a mini "base case" with common parameters to simulate how obesity (defined as BMI >30) interacts with risk, screening outcomes, and treatment effectiveness to effect trends in overall and race specific US breast cancer incidence and mortality from 1975 to 2015. We will also project results to 2025 to capture the lag time in effects of obesity (and screening). We have selected obesity as a key modifiable risk factor to be examined since it has been identified as a proximate target for change to achieve the Healthy People 2010 targets, disproportionately effects Black women, and impacts breast cancer incidence and mortality through several mechanisms with potentially competing effects. As secondary goals, we will also examine how screening policies and treatment improvements are likely to impact future differences in rates of breast cancer between Black and White women. Our objectives will be accomplished in three phases: In the first phase, we will focus on developing the common data inputs needed to address our research questions. In phase 2, we will use the models to synthesize the data and conduct analyses. Finally, in Phase 3, we will use the results to inform policy and practice relevant discussions and make the models available to others to address emerging research questions. Throughout, we will also collaborate with other CISNET modeling groups. By working together with 2 models, we can achieve important economies of scale, test the impact of model structure on results, provide a range of plausible projections, and contribute to a better understanding of the science of modeling. Overall, the information generated by these models will provide a framework to inform policy debates about equity in care and how to best achieve targeted reductions in breast cancer morbidity and mortality for all US women.